Technical Abstract:
Pedotransfer functions (PTF), which estimate soil hydraulic parameters from easy to measure soil properties, have great importance for hydrologic modeling. Classical methods, like statistical regression or artificial neural networks (ANNs) have been used for PTF development over years. Recent advances in soft learning techniques include the growing application of the data-driven method called Support Vector Machines (SVMs), which is becoming an alternative to ANN modeling tool. The aim of this work is to see whether SVM-based pedotransfer modeling may have an advantage in comparison with ANN model. Both models present the water retention PTF function, which evaluates water content for eleven pF values. Input parameters for the PTF are bulk density, sand content, and clay content. The models were developed with data from the Soil Profiles Bank of Polish Mineral Soils that includes hydraulic properties of 806 soil samples taken from 290 soil profiles. Data usable for model development were extracted from this database, and subsequently were randomly split into training (537 samples) and testing (269 samples) dataset Comparison of the root-mean-squared values for the two models showed that the SVM performance was better or the same than ANN at all soil water potentials. The largest differences in performance of the two methods were observed at low soil water potentials. SVM are usable tool for PTF model development.